[ECCV 2022] Offical implementation of the paper "Acknowledging the Unknown for Multi-label Learning with Single Positive Labels".
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Updated
Jul 11, 2024 - Python
[ECCV 2022] Offical implementation of the paper "Acknowledging the Unknown for Multi-label Learning with Single Positive Labels".
A curated list of papers on multi-label learning on graphs (MLLG).
To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data.
To deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved.
In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients.
Ensemble-based Multi-Label Neural Network (EMLNN)
An easy-to-use multi-label image dataset generator.
Metabolic pathway inference using multi-label classification with rich pathway features
Official implementation of "An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition", BMVC 2022
Convolutional Neural Network based on Hierarchical Category Structure for Multi-label Short Text Categorization
Self-Paced Multi-Label Learning with Diversity
Implementation for "AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification"
Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.
[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages
leADS: improved metabolic pathway inference based on active dataset subsampling
reMap: relabeling metabolic pathway data with groups to improve prediction outcomes
Stratification of multi-label datasets
Metabolic pathway inference using non-negative matrix factorization with community detection
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